Gender: Male 
Age: 25-34
Nationality: United States
Ethnicity: White / Caucasian;Asian / Pacific Islander 
Marital status: Single 
First language: English 
Religion: Nothing 
Education: Graduate degree
Mother's education: Doctorate or above
Father's education: Doctorate or above
Self-assessed socioeconomic status (1-10): 9
Home: Own 
Employment: Employed full time
Occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Mother's occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Father's occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Hobbies: Exercise;Use social media;Watch sports 
Devices used daily: Laptop;Smartphone 
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools used: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Dialog technology (e.g. chatbots that communicate with you such as chatGPT) 
NLP tools they would find useful: Sentiment analysis (e.g. automatically detect if user reviews are positive or negative);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language) 
Frequency of LLM use: Sometimes
LLMs used: ChatGPT 
LLM use cases: Writing (e.g., composing emails);Coding 
Contexts of LLM use: Work;Personal 
Prompt 1: In Tableau, I have two columns: Site and Stores. I want to count the number of stores per site and then create a view with the number of sites that fall into each bin of Store Count where each Store Count bin is 100 stores
Prompt 2: I am creating a pyomo model with var[a,b] (binary), param_x[a,b], and param_y. I want to count the number of a,b pairs such that var[a,b] * param_x[a,b] <= param_y.
Prompt 3:  PyomoException: Cannot convert non-constant Pyomo expression (14.537511367889147*var[a,b]  <=  6) to bool.  This error is usually caused by using a Var, unit, or mutable Param in a Boolean context such as an "if" statement, or when checking container membership or equality. For example,     >>> m.x = Var()     >>> if m.x >= 1:     ...     pass and     >>> m.y = Var()     >>> if m.y in [m.x, m.y]:     ...     pass
Prompt 4: I have a databricks notebook that is saving as an ipynb instead of .py
Prompt 5: I have an abstract Pyomo model that I'm trying to instantiate by running model.create_instance(model_data). I specify all of the parameters in model data but it tries to instantiate the objective function before populating the parameters resulting in an error
Prompt 6: I am running two pyomo models back to back and solving using Gurobi. They have the same decision variables but different parameters. I would like to warm start the 2nd model with the results from the first model
Prompt 7: I have a MIP for site selection and store allocation. I have a variable that keeps track of the number of assigned stores that fall within a given distance. When I set up the problem, I specified it needs to be integer but if I were to switch it to a real number, would that make my model easier to solve if everything else is kept the same and there are proper constraints on the value of the variable?
Prompt 8: I have a pyspark dataframe with column A. I would like to apply a pyspark function to different subsets of the dataframe grouped by the values in A
Prompt 9: I have a pyspark df of columns A, B, C, and D sorted by column D ascending. I want to get the values of A, B, C in only the first row and add them to an existing DF
Prompt 10: Is there a way to define a udf that returns a dataframe while not converting to pandas?